Coverages, JSON-LD and RDF data cubes

Many kinds of scientific data, including satellite imagery, climate simulations and sensor data, can be represented as coverages, which are essentially mappings from points in space and time to data values. Coverage data are typically encoded as multidimensional arrays in com- pact binary forms such as NetCDF, HDF and GeoTIFF, most of which require specialist knowledge and tools to process and manipulate. There is considerable current interest in helping the wider Web community use coverage data, by providing data in more general formats such as JSON and RDF, and by using commonly-accepted vocabularies. This short discussion paper outlines some current work in the area and highlights some of the main inherent issues

Automatic georeferencing of astronaut auroral photography

Astronauts on board the International Space Station (ISS) have taken thousands of high-resolution colour photographs of the aurora, which could be made useful for research if their pointing information could be reconstructed. We describe a method to do this using the star field in the images, and how the reconstructed pointing can then be used to georeference the images to a similar level of accuracy in existing all-sky camera images. We have used this method to make georeferenced auroral images taken from the ISS available and here describe the resulting data set, processing software, and how to access them

WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model

Urban land surface processes need to be represented to inform future urban‐climate and building‐energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF‐TEB. The coupling method is described generically, implemented into software, and the issue of scientific reproducibility is addressed by releasing all code and data with a Singularity image. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities. We conclude that new model couplings should make use of integration tests as meteorological evaluations by themselves are insufficient, given that errors are difficult to attribute because of the interplay between observational errors and multiple parameterization schemes (e.g. radiation, microphysics, boundary layer)

Bringing LTL Model Checking to Biologists

Abstract The BioModelAnalyzer (BMA) is a web based tool for the development of discrete models of biological systems. Through a graphical user interface, it allows rapid development of complex models of gene and protein interaction networks and stability analysis without requiring users to be proficient computer programmers. Whilst stability is a useful specification for testing many systems, testing temporal specifications in BMA presently requires the user to perform simulations. Here we describe the LTL module, which includes a graphical and natural language interfaces to testing LTL queries. The graphical interface allows for graphical construction of the queries and presents results visually in keeping with the current style of BMA. The Natural language interface complements the graphical interface by allowing a gentler introduction to formal logic and exposing educational resources

Bringing LTL Model Checking to Biologists

Abstract The BioModelAnalyzer (BMA) is a web based tool for the development of discrete models of biological systems. Through a graphical user interface, it allows rapid development of complex models of gene and protein interaction networks and stability analysis without requiring users to be proficient computer programmers. Whilst stability is a useful specification for testing many systems, testing temporal specifications in BMA presently requires the user to perform simulations. Here we describe the LTL module, which includes a graphical and natural language interfaces to testing LTL queries. The graphical interface allows for graphical construction of the queries and presents results visually in keeping with the current style of BMA. The Natural language interface complements the graphical interface by allowing a gentler introduction to formal logic and exposing educational resources

Overview of the CoverageJSON format

This Note describes CoverageJSON, a data format for describing "coverage" data in JavaScript Object Notation (JSON), and provides an overview of its design and capabilities. The primary intended purpose of the format is to enable data transfer between servers and web browsers, to support the development of interactive, data-driven web applications. "Coverage" data is a term that encompasses many kinds of data whose properties vary with space, time and other dimensions, including (but not limited to) satellite imagery, weather forecasts and river gauge measurements. We describe the motivation and objectives of the format, and provide a high-level overview of its structure and semantics. We compare CoverageJSON with other "coverage" formats and data models and provide links to tools and libraries that can help users to produce and consume data in this format. This Note does not attempt to describe the full CoverageJSON specification in detail: this is available at the project website (https://covjson.org)

Nansat: a Scientist-Orientated Python Package for Geospatial Data Processing

Nansat is a Python toolbox for analysing and processing 2-dimensional geospatial data, such as satellite imagery, output from numerical models, and gridded in-situ data. It is created with strong focus on facilitating research, and development of algorithms and autonomous processing systems. Nansat extends the widely used Geospatial Abstraction Data Library (GDAL) by adding scientific meaning to the datasets through metadata, and by adding common functionality for data analysis and handling (e.g., exporting to various data formats). Nansat uses metadata vocabularies that follow international metadata standards, in particular the Climate and Forecast (CF) conventions, and the NASA Directory Interchange Format (DIF) and Global Change Master Directory (GCMD) keywords. Functionality that is commonly needed in scientific work, such as seamless access to local or remote geospatial data in various file formats, collocation of datasets from different sources and geometries, and visualization, is also built into Nansat. The paper presents Nansat workflows, its functional structure, and examples of typical applications

Building rich and interactive web applications with CoverageJSON

Web browsers are becoming increasingly capable as visualisation and analysis platforms<br>Lots of tools and libraries are built around images and “simple features”<br>GeoJSON, KML, OpenLayers, Leaflet ...<br>Formats and tools for scientific / meteorological data are not always web-friendly<br>Complex, binary, desktop-oriented<br>Large variety, usually community-specific<br><br>=> Lots of people building ad-hoc solutions for web applications<br><br>We want to bring scientific data within the reach of more Web and mobile app developers<br>Web-friendly formats (i.e. JSON)<br>More powerful and reusable visualisation/analysis tools<br>Support for semantic content<br